"""Command: enrich - Enrich prompts with relevant skill instructions."""
from __future__ import annotations
import logging
from pathlib import Path
import click
from rich.progress import Progress, SpinnerColumn, TextColumn
from mcp_skills.cli.shared.console import console
from mcp_skills.services.prompt_enricher import PromptEnricher
from mcp_skills.services.skill_manager import SkillManager
logger = logging.getLogger(__name__)
@click.command()
@click.argument("prompt", nargs=-1, required=True)
@click.option(
"--max-skills",
default=3,
type=int,
help="Maximum number of skills to include (default: 3)",
)
@click.option(
"--detailed",
is_flag=True,
help="Include full skill instructions (default: brief summaries)",
)
@click.option(
"--threshold",
default=0.7,
type=float,
help="Relevance threshold 0.0-1.0 (default: 0.7)",
)
@click.option(
"--output",
type=click.Path(),
help="Save enriched prompt to file",
)
@click.option(
"--copy",
is_flag=True,
help="Copy enriched prompt to clipboard (requires pyperclip)",
)
def enrich(
prompt: tuple[str, ...],
max_skills: int,
detailed: bool,
threshold: float,
output: str | None,
copy: bool,
) -> None:
"""Enrich a prompt with relevant skill instructions.
Automatically finds and injects relevant skill knowledge into your prompt
to provide better context for AI assistants.
The command extracts keywords from your prompt, searches for relevant skills,
and formats an enriched prompt with skill instructions.
Process:
1. Extract keywords from prompt text
2. Search for relevant skills using hybrid RAG
3. Format enriched prompt with skill instructions
4. Optionally save to file or copy to clipboard
Examples:
# Basic enrichment with top 3 skills
mcp-skillset enrich "Create a REST API with authentication"
# Include more skills and full details
mcp-skillset enrich "Write tests for user service" --max-skills 5 --detailed
# Save to file
mcp-skillset enrich "Deploy to AWS" --output prompt.md
# Copy to clipboard
mcp-skillset enrich "Optimize database queries" --copy
# Quoted prompts for complex sentences
mcp-skillset enrich "Create a FastAPI endpoint that validates user input and returns JSON"
"""
# Join prompt tuple into single string
prompt_text = " ".join(prompt)
console.print("🔍 [bold]Enriching prompt...[/bold]\n")
console.print(f"[dim]Prompt: {prompt_text}[/dim]\n")
try:
# Initialize services
skill_manager = SkillManager()
enricher = PromptEnricher(skill_manager)
# Step 1: Extract keywords
with Progress(
SpinnerColumn(),
TextColumn("[progress.description]{task.description}"),
console=console,
) as progress:
task = progress.add_task("Extracting keywords...", total=None)
keywords = enricher.extract_keywords(prompt_text)
progress.update(task, completed=True)
console.print(
f" [green]✓[/green] Extracted keywords: {', '.join(keywords[:5])}"
)
if len(keywords) > 5:
console.print(f" [dim]... and {len(keywords) - 5} more[/dim]")
# Step 2: Search for skills
with Progress(
SpinnerColumn(),
TextColumn("[progress.description]{task.description}"),
console=console,
) as progress:
task = progress.add_task("Searching skills...", total=None)
skills = enricher.search_skills(keywords, max_skills)
progress.update(task, completed=True)
console.print(f" [green]✓[/green] Found {len(skills)} relevant skill(s)")
if not skills:
console.print(
"\n[yellow]No relevant skills found. Try different keywords or lower the threshold.[/yellow]"
)
console.print("\nSuggestions:")
console.print(" • Use more specific technical terms")
console.print(" • Try --threshold 0.5 for broader results")
console.print(" • Run: mcp-skillset search <keywords> to test")
return
# Step 3: Enrich prompt
with Progress(
SpinnerColumn(),
TextColumn("[progress.description]{task.description}"),
console=console,
) as progress:
task = progress.add_task("Enriching prompt...", total=None)
result = enricher.enrich(
prompt_text,
max_skills=max_skills,
detailed=detailed,
)
progress.update(task, completed=True)
console.print(" [green]✓[/green] Enrichment complete\n")
# Display enriched prompt
console.print("─" * 80)
console.print(result.enriched_text)
console.print("─" * 80)
# Handle output options
saved_to_file = False
copied_to_clipboard = False
if output:
try:
output_path = Path(output)
enricher.save_to_file(result.enriched_text, output_path)
console.print(f"\n[green]✓[/green] Saved to: {output_path}")
saved_to_file = True
except OSError as e:
console.print(f"\n[red]✗ Failed to save file: {e}[/red]")
if copy:
if enricher.copy_to_clipboard(result.enriched_text):
console.print("\n[green]✓[/green] Copied to clipboard")
copied_to_clipboard = True
else:
console.print(
"\n[yellow]⚠ Clipboard copy failed (install pyperclip: pip install pyperclip)[/yellow]"
)
# Summary
console.print(
f"\n[dim]Enriched with {len(result.skills_found)} skill(s) "
f"({len(skills)} candidates, threshold: {threshold})[/dim]"
)
console.print(f"[dim]Keywords: {', '.join(result.keywords[:10])}[/dim]")
if not saved_to_file and not copied_to_clipboard:
console.print(
"\n[dim]Tip: Use --output FILE to save or --copy to clipboard[/dim]"
)
except Exception as e:
console.print(f"\n[red]Enrichment failed: {e}[/red]")
logger.exception("Enrichment failed")
raise SystemExit(1)
